code: def _single_meas_grad(result, coeffs, unshifted_coeff, r0):
      """Compute the gradient for a single measurement by taking the linear combination of
      the coefficients and the measurement result.
  
      If an unshifted term exists, its contribution is added to the gradient.
      """
      if isinstance(result, list) and result == []:
          if unshifted_coeff is None:
              raise ValueError(
                  "This gradient component neither has a shifted nor an unshifted component. "
                  "It should have been identified to have a vanishing gradient earlier on."
              )  # pragma: no cover
          # return the unshifted term, which is the only contribution
          return qml.math.array(unshifted_coeff * r0)
      result = qml.math.stack(result)
      coeffs = qml.math.convert_like(coeffs, result)
      g = qml.math.tensordot(result, coeffs, [[0], [0]])
      if unshifted_coeff is not None:
          # add the unshifted term
          g = g + unshifted_coeff * r0
          g = qml.math.array(g)
      return g
  
imports: from typing import Sequence, Callable
  from functools import partial
  import numpy
  import pennylane
  from pennylane.measurements import VarianceMP
  from pennylane import transform
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from pennylane.gradients.gradient_transform import _contract_qjac_with_cjac
  from .finite_difference import finite_diff
  from .general_shift_rules import _iterate_shift_rule, frequencies_to_period, generate_shifted_tapes, process_shifts
  from .gradient_transform import _all_zero_grad, assert_no_state_returns, assert_no_tape_batching, assert_multimeasure_not_broadcasted, choose_trainable_params, find_and_validate_gradient_methods, _no_trainable_grad, reorder_grads
label_desc: Use image node_id 5 for calling a global function with example usage: _single_meas_grad(result, coeffs, unshifted_coeff, r0) and returns: g, qml